Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence

Aesthetic quality prediction is a challenging task in the computer vision community because of the complex interplay with semantic contents and photographic technologies. Recent studies on the powerful deep learning based aesthetic quality assessment usually use a binary high-low label or a numerical score to represent the aesthetic quality. However the scalar representation cannot describe well the underlying varieties of the human perception of aesthetics. In this work, we propose to predict the aesthetic score distribution (i.e., a score distribution vector of the ordinal basic human ratings) using Deep Convolutional Neural Network (DCNN). Conventional DCNNs which aim to minimize the difference between the predicted scalar numbers or vectors and the ground truth cannot be directly used for the ordinal basic rating distribution. Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization). Experimental results on large scale aesthetic dataset demonstrate the effectiveness of our introduced CJS-CNN in this task.Comment: AAAI Conference on Artificial Intelligence (AAAI), New Orleans, Louisiana, USA. 2-7 Feb. 201

VLUCI: Variational Learning of Unobserved Confounders for Counterfactual Inference

Causal inference plays a vital role in diverse domains like epidemiology, healthcare, and economics. De-confounding and counterfactual prediction in observational data has emerged as a prominent concern in causal inference research. While existing models tackle observed confounders, the presence of unobserved confounders remains a significant challenge, distorting causal inference and impacting counterfactual outcome accuracy. To address this, we propose a novel variational learning model of unobserved confounders for counterfactual inference (VLUCI), which generates the posterior distribution of unobserved confounders. VLUCI relaxes the unconfoundedness assumption often overlooked by most causal inference methods. By disentangling observed and unobserved confounders, VLUCI constructs a doubly variational inference model to approximate the distribution of unobserved confounders, which are used for inferring more accurate counterfactual outcomes. Extensive experiments on synthetic and semi-synthetic datasets demonstrate VLUCI's superior performance in inferring unobserved confounders. It is compatible with state-of-the-art counterfactual inference models, significantly improving inference accuracy at both group and individual levels. Additionally, VLUCI provides confidence intervals for counterfactual outcomes, aiding decision-making in risk-sensitive domains. We further clarify the considerations when applying VLUCI to cases where unobserved confounders don't strictly conform to our model assumptions using the public IHDP dataset as an example, highlighting the practical advantages of VLUCI.Comment: 15 pages, 8 figure

DFGC 2022: The Second DeepFake Game Competition

This paper presents the summary report on our DFGC 2022 competition. The DeepFake is rapidly evolving, and realistic face-swaps are becoming more deceptive and difficult to detect. On the contrary, methods for detecting DeepFakes are also improving. There is a two-party game between DeepFake creators and defenders. This competition provides a common platform for benchmarking the game between the current state-of-the-arts in DeepFake creation and detection methods. The main research question to be answered by this competition is the current state of the two adversaries when competed with each other. This is the second edition after the last year's DFGC 2021, with a new, more diverse video dataset, a more realistic game setting, and more reasonable evaluation metrics. With this competition, we aim to stimulate research ideas for building better defenses against the DeepFake threats. We also release our DFGC 2022 dataset contributed by both our participants and ourselves to enrich the DeepFake data resources for the research community (https://github.com/NiCE-X/DFGC-2022).Comment: Accepted by IJCB 202

Electrolyte influence on sorption behaviours of Direct Blue 71 dye on ramie fibre

Ramie loose fibre was dyed using Direct Blue 71 dye at 70, 80, 90 and 100°C without and with NaCl electrolyte in order to investigate the distinction of dye sorption behaviours. The results show that the dye exhaustion increases with addition of NaCl and shortens the equilibrium dyeing time. The dye adsorption process of dyeing without and with NaCl followed pseudo second-order kinetics, but the rate constant of sorption is larger for the latter compared to the former

A Unified Framework for Modality-Agnostic Deepfakes Detection

As AI-generated content (AIGC) thrives, deepfakes have expanded from single-modality falsification to cross-modal fake content creation, where either audio or visual components can be manipulated. While using two unimodal detectors can detect audio-visual deepfakes, cross-modal forgery clues could be overlooked. Existing multimodal deepfake detection methods typically establish correspondence between the audio and visual modalities for binary real/fake classification, and require the co-occurrence of both modalities. However, in real-world multi-modal applications, missing modality scenarios may occur where either modality is unavailable. In such cases, audio-visual detection methods are less practical than two independent unimodal methods. Consequently, the detector can not always obtain the number or type of manipulated modalities beforehand, necessitating a fake-modality-agnostic audio-visual detector. In this work, we introduce a comprehensive framework that is agnostic to fake modalities, which facilitates the identification of multimodal deepfakes and handles situations with missing modalities, regardless of the manipulations embedded in audio, video, or even cross-modal forms. To enhance the modeling of cross-modal forgery clues, we employ audio-visual speech recognition (AVSR) as a preliminary task. This efficiently extracts speech correlations across modalities, a feature challenging for deepfakes to replicate. Additionally, we propose a dual-label detection approach that follows the structure of AVSR to support the independent detection of each modality. Extensive experiments on three audio-visual datasets show that our scheme outperforms state-of-the-art detection methods with promising performance on modality-agnostic audio/video deepfakes.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

A UPLC-DAD-MS method for the quality analysis of "JiangYaBiFeng" tablet

"JiangYaBiFeng" (JYBF) tablet for treatment of hypertension in China is a composite prescription of Chinese and western medicines. By using ultra high performance liquid chromatography coupled with mass spectrometry (UPLC-MS), twenty-five compounds were simultaneously identified or tentatively characterized based on their retention times and MS spectra. Nine target compounds, hydrochlorothiazide (HC), rutin, genistin, sophoricoside, baicalin, wogonoside, genistein, baicalein and wogonin, were further quantified by ultra high performance liquid chromatography with diode-array detector (UPLC-DAD). Chromatographic separation was successfully performed on a C18 column with gradient elution of 0.1 % formic acid aqueous solution and acetonitrile at the flow rate of 0.4 mL/min in 15 min at 52 °C. Different wavelengths were used to determine corresponding compounds to ensure the best resolution. According to the methodological validation, including linearity, precision, accuracy and stability, this method was proved to be rapid, comprehensive, sensitive and feasible in the quality assessment of JYBF tablet.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Mixed Integer Programming Models for Finite Automaton and Its Application to Additive Differential Patterns of Exclusive-Or

Inspired by Fu et al. work on modeling the exclusive-or differential property of the modulo addition as an mixed-integer programming problem, we propose a method with which any finite automaton can be formulated as an mixed-integer programming model. Using this method, we show how to construct a mixed integer programming model whose feasible region is the set of all differential patterns $(\alpha, \beta, \gamma)$\u27s, such that ${\rm adp}^\oplus(\alpha, \beta \rightarrow \gamma) = {\rm Pr}_{x,y}[((x + \alpha) \oplus (y + \beta))-(x \oplus y) = \gamma] > 0$. We expect that this may be useful in automatic differential analysis with additive difference

Towards Finding the Best Characteristics of Some Bit-oriented Block Ciphers and Automatic Enumeration of (Related-key) Differential and Linear Characteristics with Predefined Properties

In this paper, we investigate the Mixed-integer Linear Programming (MILP) modelling of the differential and linear behavior of a wide range of block ciphers. We point out that the differential behavior of an arbitrary S-box can be exactly described by a small system of linear inequalities. ~~~~~Based on this observation and MILP technique, we propose an automatic method for finding high probability (related-key) differential or linear characteristics of block ciphers. Compared with Sun {\it et al.}\u27s {\it heuristic} method presented in Asiacrypt 2014, the new method is {\it exact} for most ciphers in the sense that every feasible 0-1 solution of the MILP model generated by the new method corresponds to a valid characteristic, and therefore there is no need to repeatedly add valid cutting-off inequalities into the MILP model as is done in Sun {\it et al.}\u27s method; the new method is more powerful which allows us to get the {\it exact lower bounds} of the number of differentially or linearly active S-boxes; and the new method is more efficient which allows to obtain characteristic with higher probability or covering more rounds of a cipher (sometimes with less computational effort). ~~~~~Further, by encoding the probability information of the differentials of an S-boxes into its differential patterns, we present a novel MILP modelling technique which can be used to search for the characteristics with the maximal probability, rather than the characteristics with the smallest number of active S-boxes. With this technique, we are able to get tighter security bounds and find better characteristics. ~~~~~Moreover, by employing a type of specially constructed linear inequalities which can remove {\it exactly one} feasible 0-1 solution from the feasible region of an MILP problem, we propose a method for automatic enumeration of {\it all} (related-key) differential or linear characteristics with some predefined properties, {\it e.g.}, characteristics with given input or/and output difference/mask, or with a limited number of active S-boxes. Such a method is very useful in the automatic (related-key) differential analysis, truncated (related-key) differential analysis, linear hull analysis, and the automatic construction of (related-key) boomerang/rectangle distinguishers. ~~~~~The methods presented in this paper are very simple and straightforward, based on which we implement a Python framework for automatic cryptanalysis, and extensive experiments are performed using this framework. To demonstrate the usefulness of these methods, we apply them to SIMON, PRESENT, Serpent, LBlock, DESL, and we obtain some improved cryptanalytic results

Genome sequence of the insect pathogenic fungus Cordyceps militaris, a valued traditional chinese medicine

Species in the ascomycete fungal genus Cordyceps have been proposed to be the teleomorphs of Metarhizium species. The latter have been widely used as insect biocontrol agents. Cordyceps species are highly prized for use in traditional Chinese medicines, but the genes responsible for biosynthesis of bioactive components, insect pathogenicity and the control of sexuality and fruiting have not been determined. Here, we report the genome sequence of the type species Cordyceps militaris. Phylogenomic analysis suggests that different species in the Cordyceps/Metarhizium genera have evolved into insect pathogens independently of each other, and that their similar large secretomes and gene family expansions are due to convergent evolution. However, relative to other fungi, including Metarhizium spp., many protein families are reduced in C. militaris, which suggests a more restricted ecology. Consistent with its long track record of safe usage as a medicine, the Cordyceps genome does not contain genes for known human mycotoxins. We establish that C. militaris is sexually heterothallic but, very unusually, fruiting can occur without an opposite mating-type partner. Transcriptional profiling indicates that fruiting involves induction of the Zn2Cys6-type transcription factors and MAPK pathway; unlike other fungi, however, the PKA pathway is not activated.https://doi.org/10.1186/gb-2011-12-11-r11